Video content analysis (VCA) refers to processing of video for determining events of interest and activity in a video, such as, a count of people or vehicles passing through a zone, the direction and speed of their movement, breach of boundary, speeding vehicles, etc. using computer vision techniques. VCA finds numerous applications in video surveillance, customer behavior in super markets, vehicular traffic analysis, and other areas. VCA is becoming a necessity given the extent of breach of security, requirement for surveillance, and threats to or potential compromise of human life and property in cities, defense establishments and at industrial and commercial premises.
Existing VCA algorithms process an entire image or a patch of the image using pure spatial image processing of the video. Hence, these algorithms are inherently suboptimal with respect to their computational efficiency and memory utilization. For example, processing visual graphics array (VGA) image frames of 640×480 pixels each in pure spatial domain requires segmentation of the entire 307200 pixels of each image frame, which is highly memory intensive.
Hence, there is a long felt but unresolved need for a computer implemented method and system for capturing events of interest by performing spatio-temporal analysis of a video using user-defined lines of analysis.